SoroushVahidi/scidocs huggingface dataset:
Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning
(2014)

We develop a parameter-free face recognition algorithm which is insensitive to large variations in lighting, expression, occlusion, and age using a single gallery sample per subject. We take advantage of the observation that equidistant prototypes embedding is an optimal embedding that maximizes the minimum one-against-the-rest margin between the classes. Rather than preserving the global or local structure of the training data, our method, called linear regression analysis (LRA), applies least-square regression technique to map gallery samples to the equally distant locations, regardless of the true structure of training data. Further, a novel generic learning method, which maps the intra-class facial differences of the generic faces to the zero vectors, is incorporated to enhance the generalization capability of LRA. Using this novel method, learning based on only a handful of generic classes can largely improve the face recognition performance, even when the generic data are collected from a different database and camera set-up. The incremental learning based on the Greville algorithm makes the mapping matrix efficiently updated from the newly coming gallery classes, training samples, or generic variations. Although it is fairly simple and parameter-free, LRA, combined with commonly used local descriptors, such as Gabor representation and local binary patterns, outperforms the state-of-the-art methods for several standard experiments on the Extended Yale B, CMU PIE, AR, and ∗Corresponding author. Tel:+86 10 62283059 Fax: +86 10 62285019 Email address: whdeng@bupt.edu.cn (Weihong Deng) Preprint submitted to Elsevier March 28, 2014
A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants
(2011)

This paper presents the first meta-analysis of studies that computed correlations between the h index and variants of the h index (such as the g index; in total 37 different variants) that have been proposed and discussed in the literature. A high correlation between the h index and its variants would indicate that the h index variants hardly provide added information to the h index. This meta-analysis included 135 correlation coefficients from 32 studies. The studies were based on a total sample size of N = 9005; on average, each study had a sample size of n = 257. The results of a three-level cross-classified mixed-effects metaanalysis show a high correlation between the h index and its variants: Depending on the model, the mean correlation coefficient varies between .8 and .9. This means that there is redundancy between most of the h index variants and the h index. There is a statistically significant study-to-study variation of the correlation coefficients in the information they yield. The lowest correlation coefficients with the h index are found for the h index variants MII and m index. Hence, these h index variants make a non-redundant contribution to the h index. © 2011 Elsevier Ltd. All rights reserved.
Autonomous Ground Vehicles—Concepts and a Path to the Future
(2012)

Autonomous vehicles promise numerous improvements to vehicular traffic: an increase in both highway capacity and traffic flow because of faster response times, less fuel consumption and pollution thanks to more foresighted driving, and hopefully fewer accidents thanks to collision avoidance systems. In addition, drivers can save time for more useful activities. In order for these vehicles to safely operate in everyday traffic or in harsh off-road environments, a multitude of problems in perception, navigation, and control have to be solved. This paper gives an overview of the most current trends in autonomous vehicles, highlighting the concepts common to most successful systems as well as their differences. It concludes with an outlook into the promising future of autonomous vehicles.
Acupuncture and electro-acupuncture for people diagnosed with subacromial pain syndrome: A multicentre randomized trial.
(2017)

BACKGROUND
Musculoskeletal disorders have been identified globally as the second most common healthcare problem for 'years lived with disability', and of these shoulder conditions are amongst the most common, frequently associated with substantial pain and morbidity. Exercise and acupuncture are often provided as initial treatments for musculoskeletal shoulder conditions but their clinical effectiveness is uncertain. This study compared group exercise with group exercise plus either acupuncture or electro-acupuncture in patients with subacromial pain syndrome.


METHODS
Two hundred and twenty-seven participants were recruited to a three-arm parallel-group randomized clinical trial. The primary outcome measure was the Oxford Shoulder Score. Follow-up was post treatment, and at 6 and 12 months. Between-group differences (two comparisons: the exercise group versus each of the acupuncture groups) were analysed at 6 months. A similar comparison across all follow-up time points was also conducted. Data were analysed on intention-to-treat principles with imputation of missing values.


RESULTS
Treatment groups were similar at baseline, and all treatment groups demonstrated an improvement over time. Between-group estimates at 6 months were, however, small and non-significant, for both of the comparisons. The analyses across all follow-up time points yielded similar conclusions. There was a high rate of missing values (22% for the Oxford Shoulder Score). A sensitivity analysis using complete data gave similar conclusions to the analysis with missing values imputed.


CONCLUSIONS
In the current investigation, neither acupuncture nor electro-acupuncture were found to be more beneficial than exercise alone in the treatment of subacromial pain syndrome. These findings may support clinicians with treatment planning.


SIGNIFICANCE
Shoulder pain is common and associated with substantial morbidity. Acupuncture is a popular treatment for shoulder pain. The findings suggest that acupuncture and electro-acupuncture offer no additional benefit over exercise in the treatment of shoulder pain of musculoskeletal origin.
COMPACT WIDE-SLOT TRI-BAND ANTENNA FOR WLAN/WIMAX APPLICATIONS
(2010)

In this paper, a wide-slot triple band antenna fed by a coplanar waveguide (CPW) for WLAN/WiMAX applications is proposed. The antenna mainly comprises a ground with a wide square slot in the center, a rectangular feeding strip and two pairs of planar inverted L strips (PIL) connecting with the slotted ground. By introducing the two pairs of PIL’s, three resonant frequencies, 2.4/5.5GHz for WLAN, and 3.5 GHz for WiMAX, are excited. Prototypes of the antenna are fabricated and tested. The simulated and measured results show that the proposed antenna has three good impedance bandwidths (S11 better than −10 dB) of 300MHz (about 12.6% centered at 2.39 GHz), 280 MHz (about 8% centered at 3.49GHz) and 790 MHz (about 14.5% centered at 5.43GHz), which make it easily cover the required bandwidths for WLAN band (2.4– 2.48GHz, 5.15–5.35 GHz, and 5.725–5.825GHz) and WiMAX (3.43.6GHz) applications. Moreover, the obtained radiation patterns demonstrate that the proposed antenna has figure-eight patterns in E-plane, and is omni-directional in H-plane. The gains of the antenna at operation bands are stable.
Videos as Space-Time Region Graphs
(2018)

How do humans recognize the action “opening a book”? We argue that there are two important cues: modeling temporal shape dynamics and modeling functional relationships between humans and objects. In this paper, we propose to represent videos as space-time region graphs which capture these two important cues. Our graph nodes are defined by the object region proposals from different frames in a long range video. These nodes are connected by two types of relations: (i) similarity relations capturing the long range dependencies between correlated objects and (ii) spatial-temporal relations capturing the interactions between nearby objects. We perform reasoning on this graph representation via Graph Convolutional Networks. We achieve state-of-the-art results on both Charades and Something-Something datasets. Especially for Charades, we obtain a huge 4.4% gain when our model is applied in complex environments.
Enhancing an Interactive Recommendation System with Review-based Information Filtering
(2017)

Integrating interactive faceted filtering with intelligent recommendation techniques has shown to be a promising means for increasing user control in Recommender Systems. In this paper, we extend the concept of blended recommending by automatically extracting meaningful facets from social media by means of Natural Language Processing. Concretely, we allow users to influence the recommendations by selecting facet values and weighting them based on information other users provided in their reviews. We conducted a user study with an interactive recommender implemented in the hotel domain. This evaluation shows that users are consequently able to find items fitting interests that are typically difficult to take into account when only structured content data is available. For instance, the extracted facets representing the opinions of hotel visitors make it possible to effectively search for hotels with comfortable beds or that are located in quiet surroundings without having to read the user reviews.
Hardware Covert Attacks and Countermeasures
(2016)

Computing platforms deployed in many critical infrastructures, such as smart grid, financial systems, governmental organizations etc., are subjected to security attacks and potentially devastating consequences. Computing platforms often get attacked 'physically' by an intruder accessing stored information, studying the internal structure of the hardware or injecting a fault. Even if the attackers fail to gain sensitive information stored in hardware, they may be able to disrupt the hardware or deny service leading to other kinds of security failures in the system. Hardware attacks could be covert or overt, based on the awareness of the intended system. This work classifies existing hardware attacks. Focusing mainly on covert attacks, they are quantified using a proposed schema. Different countermeasure techniques are proposed to prevent such attacks.
Model-based object tracking in monocular image sequences of road traffic scenes
(1993)

Moving vehicles are detected and tracked automatically in monocular image sequences from road traffic scenes recorded by a stationary camera. In order to exploit the a priori knowledge about shape and motion of vehicles in traffic scenes, a parameterized vehicle model is used for an intraframe matching process and a recursive estimator based on a motion model is used for motion estimation. An interpretation cycle supports the intraframe matching process with a state MAP-update step. Initial model hypotheses are generated using an image segmentation component which clusters coherently moving image features into candidate representations of images of a moving vehicle. The inclusion of an illumination model allows taking shadow edges of the vehicle into account during the matching process. Only such an elaborate combination of various techniques has enabled us to track vehicles under complex illumination conditions and over long (over 400 frames) monocular image sequences. Results on various real-world road traffic scenes are presented and open problems as well as future work are outlined.
Pedestrian Detection Using Wavelet Templates
(1997)

This paper presents a trainable object detection architecture that is applied to detecting people in static images of cluttered scenes. This problem poses several challenges. People are highly non-rigid objects with a high degree of variability in size, shape, color, and texture. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or on motion. The detection technique is based on the novel idea of the wavelet template that deenes the shape of an object in terms of a subset of the wavelet coeecients of the image. It is invariant to changes in color and texture and can be used to robustly deene a rich and complex class of objects such as people. We show how the invariant properties and computational eeciency of the wavelet template make it an eeective tool for object detection.
Fitting Parameterized Three-Dimensional Models to Images
(1991)

Model-based recognition and motion tracking depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features. This paper extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulations, variable dimensions, or surface deformations. Numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters. The LevenbergMarquardt method is used to always ensure convergence of the solution. These techniques allow model-based vision to be used for a much wider class of problems than was possible with previous methods. Their application is demonstrated for tracking the motion of curved, parameterized objects. This paper has been published in IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 5 (May 1991), pp. 441–450.
FA-RPN: Floating Region Proposals for Face Detection
(2018)

We propose a novel approach for generating region proposals for performing face-detection. Instead of classifying anchor boxes using features from a pixel in the convolutional feature map, we adopt a pooling-based approach for generating region proposals. However, pooling hundreds of thousands of anchors which are evaluated for generating proposals becomes a computational bottleneck during inference. To this end, an efficient anchor placement strategy for reducing the number of anchor-boxes is proposed. We then show that proposals generated by our network (Floating Anchor Region Proposal Network, FA-RPN) are better than RPN for generating region proposals for face detection. We discuss several beneficial features of FA-RPN proposals like iterative refinement, placement of fractional anchors and changing anchors which can be enabled without making any changes to the trained model. Our face detector based on FA-RPN obtains 89.4% mAP with a ResNet-50 backbone on the WIDER dataset.
Image Segmentation by Nested Cuts
(2000)

We present a new image segmentation algorithm based on graph cuts. Our main tool is separation of each pixel p from a special point outside the image by a cut of a minimum cost. Such a cut creates a group of pixels Cp around each pixel. We show that these groups Cp are either disjoint or nested in each other, and so they give a natural segmentation of the image. In addition this property allows an efficient implementation of the algorithm because for most pixelsp the computation of Cp is not performed on the whole graph. We inspect all Cp’s and discard those which are not interesting, for example if they are too small. This procedure automatically groups small components together or merges them into nearby large clusters. Effectively our segmentation is performed by extracting significant non-intersecting closed contours. We present interes ting segmentation results on real and artificial images.
A systematic literature review on Enterprise Architecture Implementation Methodologies
(2015)

http://dx.doi.org/10.1016/j.infsof.2015.01.012 0950-5849/ 2015 Elsevier B.V. All rights reserved. ⇑ Corresponding author. E-mail addresses: drbabak2@live.utm.my (B.D. Rouhani), mdnazrim@utm.my (M.N. Mahrin), fa.nikpay@siswa.um.edu.my (F. Nikpay), rodina@um.edu.my (R.B. npourya2@live.utm.my (P. Nikfard). Babak Darvish Rouhani a,⇑, Mohd Naz’ri Mahrin , Fatemeh Nikpay , Rodina Binti Ahmad , Pourya Nikfard c
De-Anonymisierungsverfahren: Kategorisierung und Anwendung für Datenbankanfragen(De-Anonymization: Categorization and Use-Cases for Database Queries)
(2017)

The project PArADISE deals with activity and intention recognition in smart environments. This can be used in apartments, for example, to recognize falls of elderly people. While doing this, the privacy concerns of the user should be kept. To reach this goal, the processing of the data is done as close as possible at those sensors collecting the data. Only in cases where the processing is not possible on local nodes the data will be transferred to the cloud. But before transferring, it is checked against the privacy concerns using some measures for the anonymity of the data. If the data is not valid against these checks, some additional anonymizations will be done. This anonymization of data must be done quite carefully. Mistakes might cause the problem that data can be reassigned to persons and anonymized data might be reproduced. This paper gives an overview about recent methods for anonymizing data while showing their weaknesses. How these weaknesses can be used to invert the anonymization (called de-anonymization) is shown as well. Our attacks representing the de-anonymization should help to find weaknesses in methods used to anonymize data and how these can be eliminated. Zusammenfassung: Im Projekt PArADISE sollen Aktivitätsund Intentionserkennungen in smarten Systemen, etwa Assistenzsystemen in Wohnungen, so durchgeführt werden, dass Privatheitsanforderungen des Nutzers gewahrt bleiben. Dazu werden einerseits Auswertungen der Daten sehr nah an den Sensoren, die die Daten erzeugen, vorgenommen. Eine Übertragung von Daten in die Cloud findet nur im Notfall statt. Zusätzlich werden aber vor der Übertragung der nicht vorausgewerteten Daten in die Cloud diese auf Privatheitsanforderungen hin geprüft, indem Anonymisierungsmaße getestet und eventuell weitere Anonymisierungen von Daten vorgenommen werden. Diese Anonymisierung von Datenbeständen muss mit großer Sorgfalt geschehen. Fehler können sehr schnell dazu führen, dass anonymisierte Datenbestände wieder personalisiert werden können und Daten, die eigentlich entfernt wurden, wieder zurückgewonnen werden können. Dieser Artikel betrachtet aktuelle Verfahren zur Anonymisierung und zeigt Schwachstellen auf, die zu Problemen oder gar der Umkehrung der Methoden führen können. Unsere künstlich erzeugten Angriffe durch De-Anonymisierungen sollen helfen, Schwachstellen in den Anonymisierungsverfahren zu entdecken und zu beheben.
An Ensemble Model for Diabetes Diagnosis in Large-scale and Imbalanced Dataset
(2017)

Diabetes is becoming a more and more serious health challenge worldwide with the yearly rising prevalence, especially in developing countries. The vast majority of diabetes are type 2 diabetes, which has been indicated that about 80% of type 2 diabetes complications can be prevented or delayed by timely detection. In this paper, we propose an ensemble model to precisely diagnose the diabetic on a large-scale and imbalance dataset. The dataset used in our work covers millions of people from one province in China from 2009 to 2015, which is highly skew. Results on the real-world dataset prove that our method is promising for diabetes diagnosis with a high sensitivity, F3 and G --- mean, i.e, 91.00%, 58.24%, 86.69%, respectively.
Seeded watershed cut uncertainty estimators for guided interactive segmentation
(2012)

Watershed cuts are among the fastest segmentation algorithms and therefore well suited for interactive segmentation of very large 3D data sets. To minimize the number of user interactions (“seeds”) required until the result is correct, we want the computer to actively query the human for input at the most critical locations, in analogy to active learning. These locations are found by means of suitable uncertainty measures. We propose various such measures for watershed cuts along with a theoretical analysis of some of their properties. Extensive evaluation on two types of 3D electron microscopic volumes of neural tissue shows that measures which estimate the non-local consequences of new user inputs achieve performance close to an oracle endowed with complete knowledge of the ground truth.
Electrodermal responses: what happens in the brain.
(2002)

Electrodermal activity (EDA) is now the preferred term for changes in electrical conductance of the skin, including phasic changes that have been referred to as galvanic skin responses (GSR), that result from sympathetic neuronal activity. EDA is a sensitive psychophysiological index of changes in autonomic sympathetic arousal that are integrated with emotional and cognitive states. Until recently there was little direct knowledge of brain mechanisms governing generation and control of EDA in humans. However, studies of patients with discrete brain lesions and, more recently, functional imaging techniques have clarified the contribution of brain regions implicated in emotion, attention, and cognition to peripheral EDA responses. Moreover, such studies enable an understanding of mechanisms by which states of bodily arousal, indexed by EDA, influence cognition and bias motivational behavior.
Tuning in to RF MEMS
(2009)

RF MEMS technology was initially developed as a replacement for GaAs HEMT switches and p-i-n diodes for low-loss switching networks and X-band to mm-wave phase shifters. However, we have found that its very low loss properties (high device Q), its simple microwave circuit model and zero power consumption, its high power (voltage/current) handling capabilities, and its very low distortion properties, all make it the ideal tuning device for reconfigurable filters, antennas and impedance matching networks. In fact, reconfigurable networks are currently being funded at the same level-if not higher-than RF MEMS phase shifters, and in our opinion, are much more challenging for high-Q designs.
EEG-based automatic emotion recognition: Feature extraction, selection and classification methods
(2016)

Automatic emotion recognition is an interdisciplinary research field which deals with the algorithmic detection of human affect, e.g. anger or sadness, from a variety of sources, such as speech or facial gestures. Apart from the obvious usage for industry applications in human-robot interaction, acquiring the emotional state of a person automatically also is of great potential for the health domain, especially in psychology and psychiatry. Here, evaluation of human emotion is often done using oral feedback or questionnaires during doctor-patient sessions. However, this can be perceived as intrusive by the patient. Furthermore, the evaluation can only be done in a noncontinuous manner, e.g. once a week during therapy sessions. In contrast, using automatic emotion detection, the affect state of a person can be evaluated in a continuous non-intrusive manner, for example to detect early on-sets of depression. An additional benefit of automatic emotion recognition is the objectivity of such an approach, which is not influenced by the perception of the patient and the doctor. To reach the goal of objectivity, it is important, that the source of the emotion is not easily manipulable, e.g. as in the speech modality. To circumvent this caveat, novel approaches in emotion detection research the potential of using physiological measures, such as galvanic skin sensors or pulse meters. In this paper we outline a way of detecting emotion from brain waves, i.e., EEG data. While EEG allows for a continuous, real-time automatic emotion recognition, it furthermore has the charm of measuring the affect close to the point of emergence: the brain. Using EEG data for emotion detection is nevertheless a challenging task: Which features, EEG channel locations and frequency bands are best suited for is an issue of ongoing research. In this paper we evaluate the use of state of the art feature extraction, feature selection and classification algorithms for EEG emotion classification using data from the de facto standard dataset, DEAP. Moreover, we present results that help choose methods to enhance classification performance while simultaneously reducing computational complexity.